Abstract
We proposed a new method based on supervised locality preserving projections (SLPP) for classification of high resolution satellite images. Compared with other subspace methods such as PCA and ICA, SLPP can preserve local geometric structure of data and enhance within-class local information. The proposed method has been successfully applied to IKONOS images and experimental results show that the proposed SLPP based method outperform ICA-based method. The proposed method can be practically incorporated into a GIS system.
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© 2008 Springer-Verlag Berlin Heidelberg
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Chen, YW., Han, XH. (2008). Classification of High-Resolution Satellite Images Using Supervised Locality Preserving Projections. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_19
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DOI: https://doi.org/10.1007/978-3-540-85565-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85564-4
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